Observe failures
Connect task outcomes with the prompts, evidence, and tool responses that produced them. Group failures by cause and detect new input patterns that the current evaluation does not cover.
Prioritized failure analysisContinual model improvement
Use each failure to choose the next experiment.
Deployment reveals the edge cases an initial dataset misses: new document types, ambiguous requests, and tool sequences that fail partway through. We connect those observations to a research process that selects useful examples, tests changes to the model, and carries the evidence forward into the next release.
Observed failures
Group by failure mode
Research intervention
Expert correctionDistinguish similar failures
Deduplicate · review · versionCandidate training set
Targeted examples
Test the data change in isolationProtected evaluation setReserved before experiment selection
A useful training example starts with an understood failure.
Group failure patterns, remove near-duplicates and correct the examples that matter. Keep a protected evaluation set outside the curation loop so progress remains independently measurable.
Which data would change the next model?
More usage produces more traces, but only some traces contain useful learning signal. We group failures by cause, prioritize frequent or consequential errors, and use expert corrections to construct the next dataset. Historical evaluations stay in the loop so gains on a new task do not conceal losses on work the model already handled.
The research work.
Each intervention has a purpose, a record of what changed, and a way to assess its effect.
Curate the next experiment
Select examples that test a specific improvement hypothesis. Obtain expert corrections or preferences, remove duplicates, and check provenance and overlap with protected evaluation data.
Curated dataset and experiment hypothesisEvaluate and promote
Compare the candidate with the deployed checkpoint on new and historical tasks. Promote it through a scoped rollout only after the evaluation supports the change, with the previous release available for recovery.
Model comparison and controlled releaseWhat determines
whether it works.
We set the evaluation around the application’s requirements, including the failures an aggregate score can hide.
- Feedback integrity
- Record why an example enters training, how it was reviewed, and whether its source permits the proposed use.
- Retained capability
- Measure gains on newly targeted failures together with changes on historical tasks, general instructions, and required tool behavior.
- Release stability
- Compare production task outcomes, latency, and failure rates with the previous release; investigate departures from the offline result.
What leaves
the lab.
Feedback and curation workflow
Task outcome instrumentation, a failure taxonomy, expert review queues, and rules for retaining and using feedback.
Repeatable research cycle
A sequence of testable improvement hypotheses, versioned data revisions, training runs, and checkpoint comparisons.
Controlled release process
Model promotion criteria, a staged deployment procedure, production monitoring, and a tested path to the previous release.
Before we begin.
Does the live model change after every conversation?
Our standard process collects feedback for reviewed training runs. The deployed checkpoint changes only after evaluation and release. Online learning is a separate design choice for workloads with appropriate feedback, monitoring, and recovery.
How often should we retrain?
When there is enough new signal to test a useful hypothesis. A repeated failure, a new task distribution, or a better base model may justify a run. We use those triggers and the measured value of previous updates to set the cadence.
Work with the lab
Bring us a research problem.
Tell us where the model falls short and what better performance would mean for your team.